# Chunking

Source: https://customlabs.io/glossary/chunking/
Updated: 2026-09-13

Retrieval

# Chunking

Chunking splits source documents into smaller passages before embedding, so retrieval returns focused text.

Chunk size and overlap are load-bearing decisions, not implementation details to skip past.

Chunks too large dilute relevance and blow the context budget.

Chunks too small lose the surrounding context a passage needs to make sense.

Most RAG accuracy problems trace back to chunking, not to the model.

[Insight: Your RAG demo lied to you](https://customlabs.io/insights/your-rag-demo-lied/)[Field guide: Failure modes](https://customlabs.io/failure-modes/)

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## Related terms

[Retrieval-Augmented Generation (RAG) Retrieval-Augmented Generation (RAG) retrieves relevant passages at query time and feeds them into an LLM's context.](https://customlabs.io/glossary/retrieval-augmented-generation/)[Embeddings Embeddings are numeric vectors that place similar content close together in vector space.](https://customlabs.io/glossary/embeddings/)[Vector Search (Semantic Search) Vector search finds the nearest matches to a query by comparing embeddings, not exact keywords.](https://customlabs.io/glossary/vector-search/)